numpy.apply_along_axis — NumPy v1.15 Manual (original) (raw)
numpy. apply_along_axis(func1d, axis, arr, *args, **kwargs)[source]¶
Apply a function to 1-D slices along the given axis.
Execute func1d(a, *args) where func1d operates on 1-D arrays and _a_is a 1-D slice of arr along axis.
This is equivalent to (but faster than) the following use of ndindex ands_, which sets each of ii, jj, and kk to a tuple of indices:
Ni, Nk = a.shape[:axis], a.shape[axis+1:] for ii in ndindex(Ni): for kk in ndindex(Nk): f = func1d(arr[ii + s_[:,] + kk]) Nj = f.shape for jj in ndindex(Nj): out[ii + jj + kk] = f[jj]
Equivalently, eliminating the inner loop, this can be expressed as:
Ni, Nk = a.shape[:axis], a.shape[axis+1:] for ii in ndindex(Ni): for kk in ndindex(Nk): out[ii + s_[...,] + kk] = func1d(arr[ii + s_[:,] + kk])
| Parameters: | func1d : function (M,) -> (Nj…) This function should accept 1-D arrays. It is applied to 1-D slices of arr along the specified axis. axis : integer Axis along which arr is sliced. arr : ndarray (Ni…, M, Nk…) Input array. args : any Additional arguments to func1d. kwargs : any Additional named arguments to func1d. New in version 1.9.0. |
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| Returns: | out : ndarray (Ni…, Nj…, Nk…) The output array. The shape of out is identical to the shape of_arr_, except along the axis dimension. This axis is removed, and replaced with new dimensions equal to the shape of the return value of func1d. So if func1d returns a scalar out will have one fewer dimensions than arr. |
Examples
def my_func(a): ... """Average first and last element of a 1-D array""" ... return (a[0] + a[-1]) * 0.5 b = np.array([[1,2,3], [4,5,6], [7,8,9]]) np.apply_along_axis(my_func, 0, b) array([ 4., 5., 6.]) np.apply_along_axis(my_func, 1, b) array([ 2., 5., 8.])
For a function that returns a 1D array, the number of dimensions in_outarr_ is the same as arr.
b = np.array([[8,1,7], [4,3,9], [5,2,6]]) np.apply_along_axis(sorted, 1, b) array([[1, 7, 8], [3, 4, 9], [2, 5, 6]])
For a function that returns a higher dimensional array, those dimensions are inserted in place of the axis dimension.
b = np.array([[1,2,3], [4,5,6], [7,8,9]]) np.apply_along_axis(np.diag, -1, b) array([[[1, 0, 0], [0, 2, 0], [0, 0, 3]], [[4, 0, 0], [0, 5, 0], [0, 0, 6]], [[7, 0, 0], [0, 8, 0], [0, 0, 9]]])